Recent robot car competitions and demonstrations have convincingly shown that fully autonomous vehicles are feasible with current or near future intelligent vehicle technology. This milestone creates an opportunity to reconsider modern transportation infrastructure, investigating more efficient systems that leverage a mostly autonomous vehicle population. Previous research on autonomous intersection management establishes that by leveraging the capacities of autonomous vehicles it is possible to dramatically reduce the time wasted in traffic, and therefore also fuel consumption and air pollution. In this talk, I will present an analysis of the relationship between the precision of cars' motion controllers and the efficiency of intersection controllers. I will then introduce a planning-based motion controller that can increase the efficiency of the autonomous intersection control mechanism by reducing the chance
that autonomous vehicles stop before intersections. Finally, I will describe a theoretical study of liveness properties in simplified transportation systems, and present the conditions under which no vehicle gets stuck in traffic forever.

Biography

Tsz-Chiu Au is an assistant professor at Ulsan National Institute of Science and Technology in Korea. In the past decade, he has worked on many different topics in Artificial Intelligence, including multi-agent systems, intelligent transportation systems, evolution of cooperation, and AI planning. He was a postdoctoral fellow in the Department of Computer Science at the University of Texas at Austin, and he
got his Ph.D. degree in Computer Science at the University of Maryland, College Park in 2008.